Performance Analysis and Prediction for a Free-Space Optical Communication System under Foggy Absorption  

作　　者：Jialin Wang Guanjun Xu Xiaozong Yu Zhaohui Song 

机构地区：[1]Shanghai Key Laboratory of Multidimensional Information Processing,East China Normal University,Shanghai 200241,China [2]Space Information Research Institute,Hangzhou Dianzi University,Hangzhou 310018,China [3]State Key Laboratory of Integrated Services Networks,Xidian University,Xi’an 710071,China

出　　处：《Journal of Communications and Information Networks》2023年第3期231-238,共8页通信与信息网络学报（英文）

基　　金：This research was funded by the National Natural Science Foundation of China under Grants 62271202,62027802,and 61831008;the Key Research and Development Program of Zhejiang Province under Grant 2023C01003;in part by the Open Foundation of State Key Laboratory of Integrated Services Networks Xidian University under Grant ISN23-01.

摘　　要：We analyzed the performance of a freespace optical(FSO)system in this study,considering the combined effects of atmospheric turbulence,fog absorption,and pointing errors.The impacts of atmospheric turbulence and foggy absorption were modeled using the Fisher-Snedecor F distribution and the Gamma distribution,respectively.Next,we derived the probability density function(PDF)and cumulative probability density function of the optical system under these combined effects.Based on these statistical findings,closed-form expressions for various system metrics,such as outage probability,average bit error rate(BER),and ergodic capacity,were derived.Furthermore,we used a deep neural network(DNN)to predict the ergodic capacity of the system,achieving reduced running time and improved accuracy.Finally,the accuracy of the prediction results was validated by comparing them with the analytical results.

关 键 词：FSO communication foggy absorption Fisher-Snedecor F-distribution pointing errors deep learning DNN 

分 类 号：TN9[电子电信—信息与通信工程]